[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124966-en":3,"doc-seo-124966-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124966,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Assessing Risk Factors for Heart Disease Using Machine Learning Methods","The study examines machine learning approaches for assessing cardiovascular disease risk and building predictive models from medical, laboratory, and demographic indicators. Two methods are evaluated in classification and regression settings: extreme gradient boosting (XGBoost) and a convolutional neural network (CNN). Model outputs are compared using metrics such as MSE, R², AIC, and BIC, with attention to the identification of key biomarkers including cholesterol, ferritin, homocysteine, and AST levels. Results support XGBoost’s effectiveness on tabular data while highlighting overfitting risks for CNN on validation data and the need for further optimization for sparse datasets.","Assessing risk factors for heart disease using machine learning  \nmethods  \nNatalya Maxutova1, Jamalbek Tussupov1, Kamilya Kedelbayeva2, Assemgul Tynykulova3, Zulfiya Balabayeva4, Zauresh Yersultanova5, Zhainagul Khamitova1, Kamila Zhunussova6  \n1Department of Information Systems, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana,  \nRepublic of Kazakhstan  \n2Department of Cardiology, Asfendiyarov Kazakh National Medical University, Almaty, Republic of Kazakhstan 3Higher School of Information Technology and Engineering, Astana International University, Astana, Republic of Kazakhstan 4Medical Center Hospital of the President’s Affairs Administration of the Republic of Kazakhstan, Astana, Kazakhstan 5Department of Physics, Mathematics, and Digital Technology, U. Sultangazin Pedagogical Institute Non-Profit Limited Company“Akhmet Baitursynuly Kostanay Regional University”, Kostanay, Republic of Kazakhstan 6President of Kazakhstan National Association of Nutritionists and Health Coaches, Astana, Republic of Kazakhstan  \n\n| Article history:\u003Cbr>Received May 11, 2024 Revised Jul 13, 2024 Accepted Aug 6, 2024 | This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN) . The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ability to identify key biomarkers and risk factors such as cholesterol, ferritin, homocysteine and aspartate aminotransferase (AST) levels. XGBoost parameters have been optimized for working with tabular data, demonstrating high accuracy in risk prediction. The CNN model, despite the initial reduction in error on the training set, showed signs of overfitting when analyzing validation data. Performance evaluation using the metrics of mean squared error (MSE), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) revealed significant differences between the models. The study results confirm the effectiveness of XGBoost in analyzing tabular data and summarizing risk factor knowledge, while the CNN model needs further optimization to handle sparse data. The work demonstrates the importance of choosing the right model architecture and training parameters to ensure reliable diagnosis of cardiovascular diseases.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Biochemical indicators Cardiovascular diseases Machine learning technologies Mean squared error Pathology\u003Cbr>Vanilla CNN XGBoost |  |\n\nCorresponding Author:  \nJamalbek Tussupov  \nDepartment of Information Systems, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University  \nAstana, Republic of Kazakhstan Email: [tussupov@mail.ru](tussupov@mail.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn the modern world, cardiovascular diseases (CVD) [1]–[3] remain the leading cause of death and disability among the population throughout the world, which emphasizes the need to develop more effective methods for their prevention and treatment [4]–[6] . Expanding the capabilities of medical diagnostics and prognosis through the introduction of machine learning technologies [7]–[10] opens up new prospects for early diagnosis and assessment of CVD risk [11] . The main objective of this article is to analyze various risk factors for heart disease using machine learning techniques, which can help in developing predictive models  \nto estimate the likelihood of developing CVD in individual patients. Such models are particularly valuable in clinical practice because they help optimize prevention and intervention strategies aimed at reducing the risk of developing diseases.  \nMachine learning methods, including both classic algorithms such as logistic regression [12]–[14] an","cbCaitRGktXyfaua","https://ap.wps.com/l/cbCaitRGktXyfaua","pdf",622176,1,9,"English","en",105,"# ABSTRACT\n# 1. INTRODUCTION","[{\"question\":\"Which machine learning models are used to assess heart disease risk?\",\"answer\":\"The study compares extreme gradient boosting (XGBoost) and a convolutional neural network (CNN) for risk prediction.\"},{\"question\":\"How are model performances evaluated in the study?\",\"answer\":\"Performance is measured using MSE, coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC).\"},{\"question\":\"What biomarkers and risk factors are targeted for identification?\",\"answer\":\"The work focuses on biomarkers such as cholesterol, ferritin, homocysteine, and aspartate aminotransferase (AST) levels, alongside clinical indicators.\"}]","Assessing Risk Factors for Heart Disease Using Machine Learning Methods | PDF",1785895684,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"assessing-risk-factors-for-heart-disease-using-machine-learning-methods","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/assessing-risk-factors-for-heart-disease-using-machine-learning-methods/124966/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are used to assess heart disease risk?","Question",{"text":75,"@type":76},"The study compares extreme gradient boosting (XGBoost) and a convolutional neural network (CNN) for risk prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are model performances evaluated in the study?",{"text":80,"@type":76},"Performance is measured using MSE, coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC).",{"name":82,"@type":73,"acceptedAnswer":83},"What biomarkers and risk factors are targeted for identification?",{"text":84,"@type":76},"The work focuses on biomarkers such as cholesterol, ferritin, homocysteine, and aspartate aminotransferase (AST) levels, alongside clinical indicators.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]